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Parameter estimation of an electrochemistry‐based lithium‐ion battery model using a two‐step procedure and a parameter sensitivity analysis
Author(s) -
Jin Ning,
Danilov Dmitri L.,
Van den Hof Paul M.J.,
Donkers M.C.F.
Publication year - 2018
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.4022
Subject(s) - battery (electricity) , voltage , sensitivity (control systems) , lithium ion battery , mean squared error , power (physics) , lithium (medication) , control theory (sociology) , computer science , materials science , engineering , electrical engineering , electronic engineering , mathematics , thermodynamics , statistics , physics , control (management) , medicine , endocrinology , artificial intelligence
Summary Lithium‐ion batteries are indispensable in various applications owing to their high specific energy and long service life. Lithium‐ion battery models are used for investigating the behavior of the battery and enabling power control in applications. The Doyle‐Fuller‐Newman (DFN) model is a popular electrochemistry‐based model, which characterizes the dynamics in the battery through diffusions in solid and electrolyte and predicts current/voltage response. However, the DFN model contains a large number of parameters that need to be estimated to obtain an accurate battery model. In this paper, a computationally feasible two‐step estimation approach is proposed that only uses voltage and current measurements of the battery under consideration. In the two‐step procedure, the parameters are divided into 2 groups. The first group contains thermodynamic parameters, which are estimated using low‐current discharges, while the second group contains kinetic parameters, which are estimated using a well‐designed highly‐dynamic pulse (dis‐)charge current. A parameter sensitivity analysis is done to find a subset of parameters that can be reliably estimated using current and voltage measurements only. Experimental data are collected for 12 Ah nickel cobalt aluminum pouch lithium‐ion cell. The voltage predictions of the identified model are compared with several experimental data sets to validate the model. A root mean square error between model predictions and experimental data smaller than 16 mV is achieved.